Publish-subscribe based methods and apparatuses for associating data files

ABSTRACT

Various methods and apparatuses are provided which may be implemented using one or more computing devices within a networked computing environment to employ publish-subscribe techniques to associate subscriber encoded data files with a set of publisher encoded data files.

BACKGROUND

1. Field

The subject matter disclosed herein relates to data processing using oneor more computing devices.

2. Information

Data processing tools and techniques continue to improve. Information inthe form of encoded data signals is continually being generated orotherwise identified, collected, stored, shared, and analyzed. Databasesand other like data repositories are common place, as are relatedcommunication networks and computing resources that provide access tosuch information.

The Internet is ubiquitous; the World Wide Web provided by the Internetcontinues to grow with new information seemingly being added everysecond. To provide access to such information, tools and services areoften provided which allow for the copious amounts of information to besearched through in an efficient manner. For example, service providersmay allow for users to search the World Wide Web or other like networksusing search engines. Similar tools or services may allow for one ormore databases or other like data repositories to be searched.

With so much information being available and often changing over time,there is a continuing need for methods and apparatuses that allow forcertain information to be easily identified and monitored in anefficient manner.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting and non-exhaustive aspects are described with reference tothe following figures, wherein like reference numerals refer to likeparts throughout the various figures unless otherwise specified.

FIG. 1 is a schematic block diagram illustrating an exampleimplementation of a networked computing environment comprising at leastone computing platform for use in associating subscriber encoded datafiles with publisher encoded data files, in accordance with certainexample implementations.

FIG. 2 is a schematic block diagram illustrating certain features of anexample computing device that may be used in at least one computingplatform for use in associating subscriber encoded data files withpublisher encoded data files, in accordance with certain exampleimplementations.

FIG. 3 is a flow diagram illustrating a process implementable in atleast one computing platform for use in associating subscriber encodeddata files with publisher encoded data files, in accordance with certainexample implementations.

FIG. 4 is a schematic block diagram illustrating certain features of anexample computing platform, e.g., as in FIG. 1, for use in associatingsubscriber encoded data files with publisher encoded data files, whereinthe subscriber encoded data files comprises an example informationalstory content and the publisher encoded data files comprise an examplemicro-blog content, in accordance with certain further exampleimplementations.

FIG. 5 is an illustrative diagram showing an example hierarchicalstructure representing a list of subscriber encoded data files, inaccordance with certain example implementations.

DETAILED DESCRIPTION

Various example methods and apparatuses are provided herein which may beimplemented using one or more computing platforms to associatesubscriber encoded data files with publisher encoded data files based,at least in part, on content.

As described in greater detail herein, certain example publish-subscribedata processing techniques may be implemented to allow for publisheditems (e.g., content from a set of publisher encoded data files) to beassociated with, and possibly used in annotating subscriber content(e.g., in a subscriber encoded data). By way of example, in certainimplementations, subscriber content may comprise informational storycontent, such as, e.g., news stories, reference information,announcements, advertisements, etc. It may be useful to associatesubscriber content with content from published items that may beconsidered of relevance. Thus, for example, informational story contentmay be annotated using other relevant content, such as, e.g., micro-blogcontent (e.g., from a Twitter™ source), social network content (e.g.,from a Facebook™ source), and/or other like services and/or applicationswhich may be used in a networked computing environment.

While certain example implementations are disclosed herein using newsstories as an example informational story content and Tweets fromTwitter™ as an example micro-blog content, it should be kept in mindthat claimed subject matter is not necessarily limited to such examples.Indeed, claimed subject matter is not necessarily limited to subscriberencoded data files that comprise informational story content and/orpublisher encoded data files that comprise micro-blog content and/orsocial network content.

As such, in certain example implementations, a subscriber encoded datafile and/or publisher encoded data file may comprise all or part of anytype of content that may be represented by encoded data signals, e.g.,as may be stored using a memory in an electronic device, a computerreadable medium, or the like. For example, textual content, graphicalcontent, image content, audio content, and/or other forms orcombinations of forms of content may be encoded using various knownencoding techniques. Thus, in certain example implementations, all orpart of content in a subscriber encoded data file may be of the same orsimilar form, or of a different form, from all or part of content in apublisher encoded data file. While claimed subject matter is notnecessarily limited, it may be useful, for example, to categorize orotherwise differentiate subscriber encoded data files from publisherencoded data files based, at least in part, on their respective sources,certain functions, and/or other like applicable parameters.

Attention is drawn to FIG. 1, which is a schematic block diagramillustrating an example implementation of a networked computingenvironment 100 comprising at least one computing platform 110 for usein associating subscriber encoded data files 103 with publisher encodeddata files 105, in accordance with certain example implementations.

As illustrated, networked computing environment 100 may, for example,comprise one or more subscriber content source electronic devices 102 toprovide subscriber encoded data files 103 to computing platform 110,e.g., via network 108. Networked computing environment 100 may, forexample, further comprise one or more publisher content sourceelectronic devices 104 to provide publisher encoded data files 105 tocomputing platform 110, e.g., via network 108. Networked computingenvironment 100 may, for example, further comprise a content requestingelectronic device 106 to provide a content request 107 to computingplatform 110, e.g., via network 108. Content requesting electronicdevice 106 may, for example, obtain a response 120 (e.g., to contentrequest 107) from computing platform 110, e.g., via network 108.

In certain example implementations, electronic devices 102, 104, and/or106 may represent a one or more computing platforms, one or more serversor server instances, a server farm, a cloud computing arrangement, etc.In certain example implementations, electronic devices 102, 104, and/or106 may represent a portable electronic device, such as, e.g., a cellphone, a smart phone, a laptop computer, a tablet computer, etc.

Computing platform 110 may, for example, represent one or more computingdevices, which may or may not be similar to certain electronic devices102, 104, and/or 106. FIG. 2 illustrates certain general features of acomputing device 200 that may be implemented (in whole or part) incomputing platform 110, and/or one or more of electronic devices 102,104, and/or 106.

Although illustrated as being separate, in certain instances, electronicdevices 102, 104, and/or 106 may represent the same computing device(s)and/or share certain computing and/or communication resources, orotherwise be co-located. In certain instances, one or more of electronicdevices 102, 104, and/or 106 may represent the same computing device(s)as computing platform and/or share certain computing and/orcommunication resources, or otherwise be co-located therewith.

As illustrated in an example in FIG. 1, electronic devices 102, 104, 106and computing platform 110 may be operatively coupled together via oneor more networks or other like data signal communication technologies,which may be represented using network 108. Thus, for example, network108 may comprise one or more wired or wireless telecommunication systemsor networks, one or more local area networks or the like, an intranet,the Internet, etc.

In certain example implementations, computing platform 110 may beimplemented as part of a system 130. For example, in certain instancessystem 130 may comprise or otherwise operatively support all or part ofan information retrieval (IR) system, a database system, a socialnetwork service system, a micro-blogging service system, an electronicmail service system, an information story content dissemination servicesystem, and/or the like.

As further illustrated in FIG. 1, example computing platform 110 maycomprise a subscriber index 112, a publisher index 114, a content mapper116, and a content map 118; each of which is described in greater detailherein. Subscriber index 112 may, for example, be maintained(established, updated, etc.) for a plurality of subscriber encoded datafiles 103. Publisher index 114 may, for example, be maintained(established, updated, etc.) for a plurality of publisher encoded datafiles 105. In certain example implementations, content mapper 116 maymaintain subscriber index 112 based on subscriber encoded data files 103obtained via network 108, and publisher index 114 based on publisherencoded data files 105 obtained via network 108. In certain exampleimplementations, content mapper 116 may maintain content map 118 based,at least in part, on one or more of subscriber index 112, publisherindex 114, subscriber encoded data files 103, and/or publisher encodeddata files 105.

In certain example implementations, content mapper 116 may establishresponse 120 based, at least in part, on content request 107 and contentmap 118. For example, content request 107 may identify a particularsubscriber encoded data file and content map 118 may identify anassociated set of publisher encoded data files, e.g., which may be usedannotating the particular subscriber encoded data file.

By way of example, a content request 107 may identify a particular newsstory (e.g., a subscriber encoded data file in subscriber index 112),and content map 118 may identify a set of Tweets (e.g., publisherencoded data files in publisher index 114) which have been determined bycontent mapper 116 to be of possible relevance to the particular newsstory. For example, a set of top-k ranked Tweets may be identified incontent map 118 (e.g., ranked based on indications of content relevancywith regard to a new story) and which may be used in annotating theparticular news story, where k may be an integer. Thus, for example, ifk=5 then response 120 may identify (e.g., by name, location, inclusion,etc.) up to five Tweets (e.g., publisher encoded data files) toelectronic device 106, via network 108. Of course, as with all of theexamples provided herein, claimed subject matter is not necessarily solimited.

Reference is made next to FIG. 2, which is a schematic block diagramillustrating certain features of an example electronic device 200 thatmay be used in computing platform 110 (FIG. 1) for use in associatingsubscriber encoded data files 103 with publisher encoded data files 105,in accordance with certain example implementations.

Computing device 200 may, for example, include one or more processingunits 202, memory 204, one or more connections 206, and a networkinterface 220.

Processing unit 202 is representative of one or more circuitsconfigurable to perform at least a portion of a data signal computingprocedure or process. For example, processing unit 202 may perform atleast a portion of a data signal computing procedure or processassociated with one or more of content mapper 116, content map 118, aset 210 of publisher encoded data files 105, subscriber index 112,publisher index 114, response 120, etc., e.g., as illustrated withinmemory 204. By way of example but not limitation, processing unit 202may include one or more processors, controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, and the like, or any combination thereof.

Memory 204 is representative of any data storage mechanism. Memory 204may include, for example, a primary memory 204-1 or a secondary memory204-2. Primary memory 204-1 may include, for example, a solid statememory such as a random access memory, read only memory, etc. Whileillustrated in this example as being separate from processing unit 202,it should be understood that all or part of primary memory 204-1 may beprovided within or otherwise co-located/coupled with processing unit202.

Secondary memory 204-2 may include, for example, a same or similar typeof memory as primary memory or one or more data storage devices orsystems, such as, for example, a disk drive, an optical disc drive, atape drive, a solid state memory drive, etc. In certain implementations,secondary memory 204-2 may be operatively receptive of, or otherwiseconfigurable to couple to, a computer-readable medium 230.Computer-readable medium 203 may include, for example, anynon-transitory media that can carry or make accessible data, code orinstructions 232 for use, at least in part, by processing unit 202 orother circuitry within computing device 200. Thus, in certain exampleimplementations, instructions 212 may be executable to perform one ormore functions of computing platform 110 (FIG. 1) and/or process 300(FIG. 3).

Network interface 220 may, for example, provide for or otherwise supportan operative coupling of computing device 200 to network 108 or possiblymore directly with one or more of electronic device(s) 102, 104, and/or106. By way of example, network interface 220 may comprise a networkinterface device or card, a modem, a router, a switch, a transceiver,and/or the like.

Connection(s) 206 may represent any connection(s) which may operativelycouple the illustrated features in FIG. 2. By way of way of example,connection(s) 206 may comprise one or more electrically or opticallyconductive data signal paths, one or more data buses, one or morecoupling circuits and/or devices, etc.

As illustrated in FIG. 2, at times, memory 204 may store one or moredata signals representing data and/or instructions associated withprocessing unit(s) 202, network interface 220, and/or computer-readablemedium 230. For example, all or part of one or more subscriber encodeddata files 103, and/or all or part of one or more publisher encoded datafiles 105 may be stored or otherwise identified in memory 204. Forexample, all or part of one or more content requests 107, and/or all orpart of one or more responses 120 may be stored or otherwise identifiedin memory 204. For example, a subscriber index 112 and/or a publisherindex 114 may be stored in memory 204.

For example, data and/or instructions for content mapper 116, map 118,and set 210, may be stored or otherwise identified in memory 204. Forexample, data and/or instructions for one or more ranking functions 212,and/or algorithms 214 may be stored or otherwise identified in memory204.

One or more indicators 216 may be stored in memory 204, and which mayrepresent indications of content relevancy for certain publisher encodeddata files with regard to a subscriber encoded data file. For example, acontent score 218 and/or recency score 220 may be stored in memory 204.In certain instances, a threshold 222 (e.g., a value) relating to a set210 of publisher encoded data files 105 may be stored in memory 204.

Attention is now drawn to FIG. 3, which is a flow diagram illustrating aprocess 300 implementable in at least one computing platform for use inassociating subscriber encoded data files with publisher encoded datafiles, in accordance with certain example implementations.

At example block 302, a subscriber index may be maintained for one ormore subscriber encoded data files. At example block 304, a publisherindex may be maintained for one or more publisher encoded data files.

At example block 306, for at least one of the subscriber encoded datafiles in the subscriber index, a corresponding set of publisher encodeddata files from the publisher index may be determined as beingassociated with the subscriber encoded data file. For example, incertain instances, at block 308, publisher encoded data files may beranked based, at least in part, on at least one scoring function. Someexample scoring functions are described in greater detail insubsequently sections herein. In certain example implementations, atblock 310, a set of publisher encoded data files may be determined, atleast in part, using a top-k retrieval for publish-subscribe algorithm.By way of example, as described with some examples implementations insubsequent sections, a term-at-a-time (TAAT) for publish-subscribealgorithm, and/or a skipping TAAT for publish-subscribe algorithm may beused, at least in part, to determine a set of publisher encoded datafiles at block 310. In certain other example implementations, asdescribed in subsequent sections, a document-at-a-time (DAAT) forpublish-subscribe algorithm, and/or a skipping DAAT forpublish-subscribe algorithm may be used, at least in part, to determinea set of publisher encoded data files at block 310.

At example block 312, a content map may be maintained, e.g., to identifya current set of publisher encoded data files from the publisher indexthat have been determined to be associated with a particular subscriberencoded data file. In certain example instances, a “set” may comprise atop-k or other limited number of publisher encoded data files from thepublisher index. In certain example, instances, a “set” may, at times,comprise an empty set, e.g., if no publisher encoded data files from thepublisher index have been determined to be associated with a givensubscriber encoded data file.

At example block 314, in response to a content request for a particularsubscriber encoded data file, a current set of publisher encoded datafiles from the publisher index that have been determined to beassociated with a particular subscriber encoded data file (e.g., fromcontent map), may be identified, for example, as part of a response tocontent request.

At example block 316, a “new” publisher encoded data file may beobtained. At example block 318, a subscriber index may be queried usingat least a portion of content in the new publisher encoded data file todetermine an indication of content relevancy of the new publisherencoded data file with regard to at least one subscriber encoded datafile in the subscriber index. Although not shown, in certain exampleimplementations, example block 318 may comprise all or part of exampleblock 306. At example block 320, a determination may be made as towhether a new publisher encoded data file is to be included in a set ofpublisher encoded data files based, at least in part, on an indicationof content relevancy. At example block 312, a content map may be updatedbased on a determination at block 320. For example, one or more sets ofpublisher encoded data files for one or more subscriber encoded datafiles may be affected by a new publisher encoded data file in accordancewith a determination at block 320.

At example block 322, a “new” subscriber encoded data file may beobtained. At example block 324, an initial set of publisher encoded datafiles may be determined as being associated with the new subscriberencoded data file, e.g., by querying a publisher index using at least aportion of content in the new subscriber encoded data file. Although notshown, in certain example implementations, example block 324 maycomprise all or part of example block 306. At example block 312, acontent map may be updated based on a determination at block 324. Forexample, an initial set of publisher encoded data files for a newsubscriber encoded data file may be established in a content map inaccordance with a determination at block 320.

Reference will be made next to FIG. 4, which is a schematic blockdiagram illustrating certain features of an example computing platform,e.g., as in FIG. 1, for use in associating subscriber encoded data fileswith publisher encoded data files, e.g., as in FIG. 3, wherein theexample subscriber encoded data files comprise informational storycontent in the form of news stories and the publisher encoded data filescomprise micro-blog content in the form of Tweets (e.g., Twitter™updates), in accordance with certain further example implementations. Aswith all of the examples provided herein, claimed subject matter is notnecessarily limited to any of the example implementations.

Before addressing FIG. 4 in detail, it may be instructive to firstreview an example schema wherein it may prove beneficial to annotate newstories available via the Internet with recently Tweeted content.

Social content, such as Twitter™ updates (Tweets), may providefirst-hand reports of news events, as well as numerous commentaries thatare indicative of a public view of such events. As such, social updatesmay provide a good complement to other news stories. However, it may bedifficult to annotate certain news stories with social updates (Tweets),at a news website serving a high volume of page views. For example,there may be a significantly high rate of both the page views (e.g.,millions to billions a day) and of incoming Tweets (e.g., more than 100millions a day), which may make even near real-time indexing of Tweetsineffective, as traditional techniques may require the use of an indexthat is both queried and updated extremely frequently. Moreover, alikely rate of Tweet updates may render traditional caching techniquesalmost unusable since a cache would likely become stale very quickly.

As presented herein, example methods and apparatuses may be implementedin which each news story may be treated as a subscription for Tweetswhich may be relevant to a story's content. As described herein, certainexample algorithms may be implemented that may more efficientlyassociate (e.g., match) Tweets to stories, e.g., to proactively maintaina set of top-k Tweets for an applicable story. It is believed thatcertain example algorithms may be implemented in a manner which tends toconsume only a small fraction of a computing resource cost of certaintraditional solutions. Furthermore, it is believed that certain examplealgorithms may be applicable to other large scale content-basedpublish-subscribe situations.

Micro-blogging services such as, e.g., those provided by Twitter™ andother service providers, may be a useful part of a news consumptionexperience on the World Wide Web, and/or other networks. With over 100million reported users, Twitter™ often provides some of the quickestfirst-hand reports of news events, as well as numerous commentaries thatmay be indicative of certain views of news events. As such, thereappears to be a desire to combine traditional and social news contentthrough annotation, e.g., annotating news stories with relatedmicro-blogs, such as, Twitter™ updates (Tweets).

However, there may be several technical difficulties in building anefficient system for such social news annotation. One of the challengesis that Tweets may arrive in very high volume, e.g., more than 100million per day. As recency may be one of the indicators of relevancefor Tweets, news stories may be improved if annotated quickly, e.g., innear real time. Furthermore, large news websites may have significantlyhigh numbers of page views which may provide for a better userexperience if served with low latency (e.g., fractions of a second). Inthis context it may be that a system may receive hundreds of millions tobillions of content requests in a day. Also, there may be a non-trivialnumber of unique stories to consider annotating, e.g., possibly rangingin hundreds of thousands.

In accordance with certain aspects of the present description, exampletop-k publish-subscribe approaches may be adapted and implemented whichmay efficiently associate news stories with social content, possibly innear real-time. To be able to cope with a high volume of updates(Tweets) and story content requests, the techniques presented by way ofexamples herein may use news stories as subscriptions, and Tweets aspublished items in a publish-subscribe approach.

In certain traditional publish-subscribe approaches published itemstrigger subscriptions when they match a subscription's predicate. Incertain example top-k publish-subscribe approaches provided herein eachsubscription (story) scores published items (Tweets), for example, basedon a content overlap between the story and the Tweets. A subscriptionmay, for example, be triggered by a new published item if the itemscores higher than a k-th top scored item (threshold), e.g., previouslypublished (determined) for this specific subscription. In certainexample top-k publish-subscribe approaches provided herein, a ranked setof published items may be provided for each subscription as opposed to aranked list of subscriptions for a given item.

By way of example, a current result set of top-k items may be maintainedfor each story, e.g., in a content map which may reduce a story servingcost to an in-memory table lookup made to fetch an applicable set. Assuch, in certain example implementations, on an arrival of a “new”Tweet, a process may be implemented (e.g., possibly in the background)to identify stories that the new Tweet is related to, and to adjusttheir result sets accordingly. An example process was illustrated inFIG. 3 which maintained a content map in similar fashion.

In certain example top-k publish-subscribe approaches provided herein,news annotation may be more feasible from efficiency standpoint usingcertain scoring functions. Some example scoring functions are describedin detail below, however, it should be recognized that various othertechniques may be employed or adapted for use in other implementations.For example, it is believed that language model scoring techniquesand/or the like may be employed.

Certain example top-k publish-subscribe approaches provided herein maybe particularly suitable for high volume updates and requests more thana traditional “pull” approach, where Tweets may be indexed usingreal-time indexing and news page view requests may be issued as queriesat serving time.

Additionally, certain example top-k publish-subscribe approachesprovided herein may be applicable to other publish-subscribe scenariosbeyond news annotation with Tweets. For example, certain example top-kpublish-subscribe approaches provided herein may be applicable wheresubscriptions are triggered not only based on a predicate match, butalso on their relationship with previously considered items. Someexamples may include content-based RSS feed subscriptions, systems forcombining editorial and user generated content under high query volume,updating cached results of “head” queries in a search engine, to namejust a few. Even in cases where a stream of published items may not beas high as in the case of Twitter™, certain example top-kpublish-subscribe approaches provided herein may offer a lower servingcost since processing may be done on arrival of published items, whileat query time a pre-computed result may be quickly obtained from memory.Another potential advantage to certain example top-k publish-subscribeapproaches provided herein may be that matching may occur “off-line”,e.g., and possibly using various complex matching algorithm(s) and/orfunction(s).

In certain example top-k publish-subscribe approaches provided hereincertain example document-at-a-time (DAAT) and/or term-at-a-time (TAAT)algorithms may be adapted to support a publish-subscribe setting.Moreover, it is believed that with certain adaptations (e.g., skipping)provided in some example top-k publish-subscribe algorithms, furthersignificant reductions in processing time may be provided. For example,it appears that a reduction in processing time may be provided bymaintaining “threshold” scores which new Tweets would need to exceed inorder to be included in current result sets of stories. Thus, forexample, if an upper bound on a Tweet's relevancy score appears to bebelow a threshold, then it may be possible skip a full computation ofStory-Tweet score. Hence, score computation may be part of a processingcost and thus by skipping a significant fraction of score computationsit may be possible to reduce processing resource usage and/or processingtime of an incoming Tweet accordingly. Thus, in accordance with certainaspects, maintaining thresholds for ranges of stories may allow forcertain DAAT and/or TAAT skipping adaptations which may provide asignificant reduction in processing latency.

In certain example top-k publish-subscribe approaches provided herein,subscriptions may be triggered based on a score assigned to publisheditems and their relationship with previous items. Thus, as shown by somedetailed examples below, a top-k publish-subscribe paradigm may be usedfor associating news stories with Tweets (possibly in near real time) byindexing news stories as subscriptions and processing Tweets aspublished items, allowing for much lower cost of serving.

By way of example, let us consider a news website serving a collection Sof news stories. A story served at time t may be associated with a setof k most relevant social updates (Tweets) received up to time t.Formally, given a set U^(t) of updates at serving time t, story s may beassociated with a set of top-k updates R_(s) ^(t) (note, superscripts tare omitted when clear from the context) according to the followingscoring function:score(s,u,t)=cs(s,u)·rs(t,t _(u)),

where cs is a content-based score function, rs is a recency scorefunction, and t_(u) is a creation time of update u. In this example, letus assume that cs may be from a family of IR scoring functions, such as,e.g., a cosine similarity or a BM25, and rs to monotonically decreasewith t−t_(u), e.g., at the same rate for all Tweets. Thus, one maydetermine that a Tweet u may be related (e.g., be relevant) to story sif cs(s, u)>0.

Let us consider an example content-based score function, based on twopopular IR relevance functions: cosine similarity and BM25. Let us adopta variant of cosine similarity similar to the one used in theopen-source Lucene search engine

${{{cs}\left( {s,u} \right)} = {\sum\limits_{i}\;{u_{i} \cdot {{idf}^{2}(i)} \cdot \sqrt{\frac{s_{i}}{s}}}}},$where s_(i) (resp. u_(i)) is a frequency of term i in a content of s(resp. u), |s| is a length of S, and

${{idf}(i)} = {1 + {\log\left( \frac{S}{1 + {\left\{ {{s \in S}❘{s_{i} > 0}} \right\} }} \right)}}$is an inverse document frequency of i in S. With slight adjustment innotation one may refer to a score contribution of an individual term bycs(s; u_(i)), e.g., in the above function

${{cs}\left( {s,u_{i}} \right)} = {u_{i} \cdot {{idf}^{2}(i)} \cdot {\sqrt{\frac{s_{i}}{s}}.}}$

An example BM25 content-based score function may defined as follows:

${{{cs}\left( {s,u} \right)} = {\sum\limits_{i}{u_{i} \cdot {{idf}(i)} \cdot \frac{s_{i} \cdot \left( {k_{1} + 1} \right)}{s_{i} + {k_{1} \cdot \left( {1 - b + {b \cdot \frac{s}{{avg}_{s \in S}{s}}}} \right)}}}}},$where k₁ and b are parameters of a function (e.g., in some examples maybe k₁=2; b=0.75).

While these example content-based score functions may be considered bysome to be simplistic scoring functions, they are based onquery-document overlap and may be implemented as dot products similarlyto other popular scoring functions, and may therefore incur similarruntime costs. In certain instances, it may be beneficial to employmultiple or more complex content-based score functions. For example, itmay be useful in certain implementations to employ scoring functionsthat may be used in first phase retrieval, after which a second phasemay employ a more elaborate scoring function to produce a final orderingof results.

Let us now consider an example recency score. As mentioned, Tweets areoften tied (explicitly or implicitly) to some specific event, andcontent relevance to current events may decline as time passes. Inaccordance with certain aspects, it is believed that in certainimplementations, one may therefore discount scores of older Tweets bysome factor. By way of example, in certain implementations scores ofTweets may be discounted by a factor of two every time-interval τ (aparameter). By way of example, one may use an exponentially decayingrecency score:

${{rs}\left( {t_{u},t} \right)} = {2^{\frac{t_{u} - t}{\tau}}.}$

In certain example implementations, a monotonically decreasing functionmay be used.

Let us now consider an example top-k publish-subscribe approach that mayprovide for a scalable solution while keeping page view processing costslow. As previously illustrated, one potential way to keep page viewprocessing costs low may be to maintain a content map indicating acurrent set of Tweets that may be used in annotating a story. Let R_(s)be a set of current top-k Tweets for a story sεS (e.g., at time t,comprising the top-k Tweets from u′). For each “new” Tweet one mayidentify stories that a new Tweet may annotate, and may include the newTweet to applicable stories' result sets. On page views (e.g., inresponse to a contest request), a pre-computed annotations R_(s) may beaccessed directly and/or with only minor additional processing overhead.

Attention is now directed to FIG. 4, which illustrates certain featuresof an example top-k publish-subscribe system 400 for new stories andTweets. FIG. 4 is similar to FIG. 1. and as such may be implemented inone or more computing devices in one or more computing platforms 110.

As illustrated, a complementary Tweet Index 114 may be maintained andused to initialize annotations of new stories 103 that are being addedto the system. A story Index 112 may also be provided to index storiesin S. A content mapper 116 may be used for example to maintain StoryIndex 112 and Tweet Index 114. As represented by line 406, contentmapper 116 may also query Story Index 112 using a “new” Tweet 105.Content mapper 116 may also update a current top-k Tweets R_(s) for eachstory, as applicable.

Similar to example process 300 (FIG. 3), example top-k publish-subscribesystem 400 may: (1) handle a “new” story by querying Tweet Index 114(e.g., as represented by line 404) and retrieving the top-k Tweets,which may be used to initialize R_(s); (2) handle a new Tweet 105 byquerying story Index 112 and, for every story s, if the new Tweet ispart of a top-k results for s, may include the new Tweet in R_(s); (3)in response to a content request (e.g., as part of a page view 402),identify the top-k set of Tweets R_(s). In this example, the top-k setof Tweets R_(s) may be maintained by content mapper 116 in a story totop-k Tweets content map 118.

Accordingly, in this example, Story Index 112 may be queried frequently,but updated infrequently, while Tweet Index 114 may be updated morefrequently but queried only for new stories which may be orders ofmagnitude less frequent than the number of Tweet updates. Additionally,in this example, page views, which may be the most frequent event, maybe served very efficiently by response 120 returning pre-computed set ofTweets R_(s), e.g., as identified in map 118.

Let us now further consider an example subscriber index (e.g., StoryIndex 112). As mentioned, Story Index 112 may be used to index storiesinstead of Tweets, and to run Tweets as queries on that index. Invertedindices may be one of the most popular data structures for informationretrieval. For example, with an inverted index, subscriber content ofdocuments (new stories in the example of FIG. 4) may be indexed in aninverted index structure, which may be a collection of posting lists L₁,L₂, . . . /L_(m), e.g., corresponding to terms (or, more generally,features) in the story corpus. A list L_(i) may, for example, comprisepostings of a form

s, ps(s, i)

for each story that contains term i, where s may be a story identifierand

${p\;{s\left( {s,i} \right)}} = \frac{{cs}\left( {s,u_{i}} \right)}{u_{i}}$may be a partial score—a score contribution of term i to a full scorecs(s,•). For example, for cosine similarity,

${p\;{s\left( {s,i} \right)}} = {{idf}^{2} \cdot {\sqrt{\frac{s_{i}}{s}}.}}$A factor u_(i) may multiply a partial score at an evaluation time givingcs(s,u_(i)). Thus, given a query using a published item (e.g., Tweet) u,a scoring function cs, and k, an example IR retrieval algorithm, shownin example conventional Algorithm 1 (below), traverses an inverted indexof a corpus S and returns a top-k stories for u, that is, stories in Swith a highest value of cs(s,u).

Algorithm 1 Generic conventional IR top-k retrieval algorithm 1: Input:Index of S 2: Input: Query u 3: Input: Number of results k 4: Output:R - min-heap of size k 5: Let L₁, L₂,..., L_(|u|) be the posting listsof terms in u 6: R ← Ø ; 7: for every story s ∈ ∪L_(i) do 8: Attemptinserting (s; cs(s, u)) into R 9: return R

Note that the above described semantics may be different from what onemay want to achieve, especially in a news story—Tweet example. Forexample, as mentioned, one may not want to find the top-k stories for agiven Tweet, but rather all stories for which a Tweet is among the top-kTweets. This difference may therefore preclude using off-the-shelfretrieval algorithms.

Consider instead, example Algorithm 2 (below) which shows top-kpublish-subscribe semantics. In this example, given a Tweet u andcurrent top-k sets for all stories R_(s) ₁ , R_(s) ₂ , . . . , R_(s)_(n) , a new Tweet u may be included into result sets for which u ranksamong the top-k matching Tweets. Note that in this example, we ignore arecency score rs.

Algorithm 2 Example adapted publish-subscribe based algorithm 1: Input:Index of S 2: Input: Query u 3: Input: R_(s) ₁ , R_(s) ₂ ,..., R_(s)_(n) —min-heaps of size k for all stories in S 4: Output: Updatedmin-heaps R_(s) ₁ , R_(s) ₂ ,..., R_(s) _(n) 5: Let L₁, L₂,..., L_(|u|)be the posting lists of terms in u 6: for every story s ∈ ∪L_(i) do 7:Attempt inserting (u; cs(s, u)) into R_(s) 8: return R_(s) ₁ , R_(s) ₂,..., R_(s) _(n)

Let us now further consider the use of an example recency function inscoring. Recall that an example recency score function

${{{rs}\left( {t_{u},t} \right)} = 2^{\frac{t_{u} - t}{\tau}}},$may decay exponentially with a time gap between a creation time of Tweett_(u) and a page view time t. Accordingly, it may be generally observedthat, as t grows, a relative ranking between scores of past Tweets maynot change. Hence, one may not need to re-compute scores and re-rankTweets in R_(s) between updates caused by new Tweets. However, it mightseem that whenever one attempts to insert a new Tweet into R_(s), onemay have to re-compute scores of Tweets that are already in R_(s) inorder to be able to compare these scores to the score of the new Tweet.Fortunately, this re-computation may be avoided by considering a recencyscore as

${{{rs}\left( {t_{u},t} \right)} = \frac{2^{t_{u}/\tau}}{2^{t/\tau}}},$and noting that the denominator 2^(t/τ) depends only on a current timet, and at any given time is equal for all Tweets and all stories. Thus,if we do not use absolute score values beyond relative ranking ofTweets, we can replace 2^(t/τ) with a constant=1, leading to thefollowing example recency function:rs(t _(u))=2^(t) ^(u) ^(/τ).

The above example recency function depends on a creation time of a Tweetand thus may not have to be recomputed later as one attempts to insertnew Tweets. In certain instances, however, if scores may growexponentially as new Tweets arrive, the scores may grow beyond availablenumerical precision, in which case a pass over all Tweets in all R_(s)may be preformed, subtracting a constant from all values of t_(u) andre-computing the scores.

To detach accounting for a recency score from a retrieval algorithm, asa new Tweet arrives one may compute its rs(t_(u)) and use rs(t_(u)) as amultiplier of term weights in a Tweet's query vector u, e.g., one mayuse 2^(t) ^(u) ^(/τ)·u to query an inverted Story Index. In computing aTweet's content based score cs with a query vector, one may get adesired example final score:

${{cs}\left( {s,{2^{t_{u}/\tau} \cdot u}} \right)} = {{\sum\limits_{i}{2^{t_{u}/\tau} \cdot {{cs}\left( {s,u_{i}} \right)}}} = {{2^{t_{u}/\tau} \cdot {{cs}\left( {s,u} \right)}} = {{{score}\left( {s,u,t} \right)}.}}}$

Let us now consider some example retrieval algorithms for certain top-kpublish-subscribe approaches. In this section it can be seen thatcertain adaptations may be made to known top-k retrieval techniques toallow for their use in an example publish-subscribe setting.

With this in mind, let us first consider an example implementation of apublish-subscribe retrieval algorithm (Algorithm 2) using aterm-at-a-time (TAAT) strategy. One may refer to this example as a TAATfor publish-subscribe algorithm.

In term-at-a-time algorithms, posting lists corresponding to query termsmay be processed sequentially, while accumulating partial scores of alldocuments encountered in the lists. After traversing all the lists,accumulated scores may be equal to a full query-document scores(cs(s,u)); documents that did not appear in any of the posting lists mayhave a zero score.

A top-k retrieval algorithm may then pick k documents with highestaccumulated scores and return them as query result. In the presentexample setting, where a query may be a Tweet and documents may bestories, a new Tweet u may end up being added to R_(s) of any story sfor which score(s, u, t)>0. Thus, instead of picking the top-k storieswith highest scores, we attempt to add u into R_(s) of all storieshaving positive accumulated score, as shown in Algorithm 3 (below),where μ_(s) denotes a threshold score of a Tweet in R_(s) (recall thatu_(i) denotes a term weight of term i in Tweet u).

Algorithm 3 Example TAAT for publish-subscribe algorithm  1: Input:Index of S  2: Input: Query u  3: Input: R_(s) ₁ , R_(s) ₂ ,..., R_(s)_(n) —min-heaps of size k for all stories in S  4: Output: Updatedmin-heaps R_(s) ₁ , R_(s) ₂ ,..., R_(s) _(n)  5: Let L₁, L₂,..., L_(|u|)be the posting lists of terms in u, in the descending  order of theirmaximal score  6: A[s] ← 0 for all s — Accumulators vector  7: for i ∈[1, 2,..., |u|] do  8: for 

 s, ps(s,i) 

 ∈ L_(i) do  9:  A[s] ← A[s] + u_(i) · ps(s,i) 10: for every s such thatA[s] > 0 do 11: μ_(s) ← min. score of a Tweet in R_(s) if |R_(s)| = k, 0otherwise 12: if μ_(s) < A[s] 

 s < A[s] then 13: if |R_(s)| = k, then 14: Remove the least scoredTweet from R_(s) 15: Add (u, A[s]) to R_(s) 16: return R_(s) ₁ , R_(s) ₂,..., R_(s) _(n)

Next, let us first consider an example implementation of an adaptedpublish-subscribe retrieval algorithm (e.g., Algorithm 2) using aterm-at-a-time (TAAT) strategy with skipping. One may refer to thisexample as a skipping TAAT for publish-subscribe algorithm.

An optimization often implemented in retrieval algorithms is skippingsome postings or entire posting lists when scores computed so farindicate that no documents in a skipped postings may make it into aresult set. For example, let ms(L_(i))=max, ps(s, i) be a maximalpartial score in list L_(i). An example known algorithm may sort postinglists in a descending order of their maximal score, and process themsequentially until either exhausting all lists or satisfying anearly-termination condition, in which case remaining lists may beskipped and a current top-k results may be returned. Anearly-termination condition may ensure that no documents other than acurrent top-k may make it into a true top-k result of a query. Thiscondition may, for example, be satisfied if a k-th highest accumulatedscore is greater than an upper bound on the scores of other documentsthat are currently not among the top-k ones, calculated as a (k+1)-thhighest accumulated score plus the sum of maximal scores of theremaining lists. Thus, let a next list to be evaluated be L_(i), anddenote by A_(k) a k-th highest accumulated score. Then, lists L_(i),L_(i+1), . . . , L_(|u|) may be safely skipped if

$A_{k} > {A_{k + 1} + {\sum\limits_{j \geq i}{{u_{j} \cdot m}\;{{s\left( L_{j} \right)}.}}}}$

With this in mind, in our example setting, since we are not beinterested in top-k stories but rather top-k Tweets for each story, wecannot use the above condition and have instead developed a differentcondition suitable to our example. In order to skip list L_(i), we maymake sure that Tweet u does not make it into R_(s) of any story s inL_(i). In other words, an upper bound on a score of u may be below μ_(s)for every SεL_(i):

$\begin{matrix}{{A_{1} + {\sum\limits_{j \geq i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}}} \leq {\min\limits_{s \in L_{i}}{\mu_{s}.}}} & {{Condition}\mspace{14mu}(1)}\end{matrix}$

Should Condition (1) not hold, we may process L_(i) as shown inAlgorithm 3, lines 8-9, for example. Should Condition (1) hold, we mayskip list L_(i) and proceed to list L_(i+1), and may again checkCondition (1), and so on, for example. Note that such skipping may makesome accumulated scores less accurate (e.g., lower than they should be).Observe however, that these may be scores of exactly the stories inL_(i) that we skipped because Tweet u would not make it into their R_(s)sets even with a full score. Thus, making an accumulated score of thesestories lower may not change the overall outcome of the algorithm.

In certain example implementations, although Condition (1) may allowsone to skip a whole list it L_(i), it may be less likely to hold forlonger lists, while skipping such lists may make a greater differencefor the evaluation time. In certain instances, even a single story withμ_(s)=0 at a middle of a list may prevent skipping that list. As such,in certain instances one may resort to a more fine-grained skippingstrategy. For example, one may skip a segment of a list until a firststory violates Condition (1), i.e., first s in L_(i) for which

${A_{1} + {\sum\limits_{j \geq i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}}} > {\mu_{s}.}$One may then process that story by updating its score in theaccumulators (line 9 in Algorithm 3), and then again look for the nextstory in the list that violates the condition (1). Thus, one may, forexample, use a primitive next(L_(i), pos, UB) in which, e.g., given alist L_(i), a starting position pos in that list, and a value of

${{UB} = {A_{1} + {\sum\limits_{j \geq i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}}}},$returns a next story s in L_(i) such that

${A_{1} + {\sum\limits_{j \geq i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}}} > {\mu_{s}.}$

Note that next(L_(i),pos,UB) may, for example, be more efficient thanjust traversing stories in L_(i) and comparing their μ_(s) to UB, asthis may take the same or a similar number of steps as an originalalgorithm might perform traversing L_(i). As such, one may use atree-based data structure for each list L_(i) that supports twooperations: next(pos,UB) corresponding to a next primitive (e.g., asdefined above), and update(s,μ_(s)), e.g., that updates a data structurewhen μ_(s) of a story s in L_(i) changes. More specifically, for everyposting list L_(i) one may build a balanced binary tree I_(i) whereleafs represent the postings S₁, S₂, . . . , S_(|L) _(i) _(|) in L_(i)and store their corresponding μ_(s) values. Each internal node n inI_(i) may store n·μ_(s), the minimum μ value of its sub-tree, forexample. A subtree rooted at n may include postings with indices in arange n.range_start to n.range_end, and as such one may consider that nis responsible for these indices. By way of example, FIG. 5 shows apossible tree I_(i) 500 for an L_(i) with five postings.

Example Algorithm 4 (below) presents a pseudo-code for operationnext(pos, UB) of a tree I_(i).

Algorithm 4 Example pseudo-code for operation next of tree I_(i)  1:Input: pos ∈ [1,|L_(i)|]  2: Input: UB  3: Output: next(L_(i), pos, UB) 4: endIndex←findMaxInterval(I_(i).root)  5: if (endIndex = |L_(i)|)return ∞ //skip remainder of L_(i)  6: if (endIndex = ⊥) return pos //noskipping is possible  7: return endIndex + 1  8: procedurefindMaxInterval(node)  9: if (node.μ > UB) return node.range_end 10: if(isLeaf(node)) return ⊥ 11:  p ←⊥ 12: if (pos ≦ node.left.range_end)then 13:  p ← findMaxInterval(node.left) 14: if (p <node.left.range_end) return p 15:  q ←findMaxInterval(node.right) 16: if(q ≠⊥) return q 17: return p

Example Algorithm 4 uses a recursive subroutine findMaxInterval, whichgets a node as a parameter (and pos and UB as implicit parameters) andreturns endIndex—the maximal index of a story s in L_(i) which appearsat least in position pos in L_(i) and for which μ_(s)≧UB (e.g., this maybe the last story that may safely be skipped). If node.μ>UB (line 9),all stories in a sub-tree rooted at node may be skipped. Otherwise, wecheck whether pos is smaller than the last index for which node's leftson is responsible (line 12). If so, we may, for example, proceed byfinding a maximal index in the left subtree that may be skipped, e.g.,by invoking findMaxInterval recursively with node's left son as theparameter. If a maximal index to be skipped may not be the last innode's left subtree (line 14) we may not skip any postings in the rightsubtree. If all postings in the left subtree may be skipped, or in casepos is larger than all indices in node's left subtree, a last posting tobe skipped may be in node's right subtree. We therefore may proceed byinvoking findMaxInterval with node's right son as the parameter.

In a situation where skipping may not be possible, a top-level call tofindMaxInterval may return ⊥, and next in turn may return pos. IffindMaxInterval returns a last index in L_(i), next may return ∞,indicating that we may skip over all postings in L_(i). Otherwise, forexample, any position end Index returned by findMaxInterval may be thelast position that may be skipped, and thus next may return endIndex+1.

Although findMaxInterval may, for example, proceed by recursivelytraversing both the left and the right son of node (e.g., in lines 13and 15, respectively), observe that the right sub-tree may be traversedin two cases: 1) if the left sub-tree is not traversed, i.e., thecondition in line 12 evaluates to false, or 2) if the left son isexamined but the condition in line 9 evaluates to true, indicating thatthe whole left sub-tree can be safely skipped. In both cases, atraversal may examine the left son of a node, but may not go any deeperinto the left sub-tree. Thus, for example, next(pos;UB) may takeO(log|L_(i)|) steps. Further, update(s,μ_(s)) may, for example, beperformed by finding a leaf corresponding to s and updating the μ valuesstored at each node in a path from this leaf to the root of I_(i).

In an attempt to reduce memory footprint, one may, for example, embed atree into an array of size 2|L_(i). In certain example implementations,one may attempt to reduce memory footprint further by making each leafin I_(i) responsible for a range of l consecutive postings in L_(i)(instead of a single posting) and use a lowest μ_(s) of a story in thisrange as a value stored in the leaf. While this example modification mayslightly reduce a number of postings an algorithm skips, it may reducethe memory footprint of trees by a factor of l or a lookup complexity byO(log l), which may be overall beneficial in certain implementations. Byway of example, in certain example implementations it is believed that avalue of l in a range of between about 32 and about 1024 (e.g.,depending on the index size) may result in an acceptablememory-performance tradeoff.

An example skipping TAAT for publish-subscribe algorithm is provided inAlgorithm 5 (below). Example Algorithm 5 maintains a set l of suchtrees, consults it to allow skipping over intervals of posting list(e.g., as described above), and updates affected trees once μ_(s) forsome s changes. Note that if such change occurs, one may update alltrees which contain s (e.g., see example Algorithm 6, lines 9 and 10,which shows a procedure that attempts to insert a Tweet u into R_(s) andupdates trees). Enumerating these trees may be considered equivalent tomaintaining a forward index whose size may be of a same order as a sizeof an inverted index of S.

To increase skipping in certain example implementations, one may use anoptimization of ordering story ids, e.g., in an ascending order of theirμ_(s). This may reduce chances of encountering a “stray” story with lowμ_(s) in a range of stories with high μ_(s) in a posting list, thuspossibly allowing for longer skips. Such a (re)ordering may, forexample, be performed periodically, as μ_(s) of stories change.

Algorithm 5 Example skipping TAAT for publish-subscribe algorithm  1:Input: Index of S  2: Input: Query u  3: Input: R_(s) ₁ , R_(s) ₂ , . .. , R_(s) _(n) —min-heaps of size k for all stories in S  4: Output:Updated min-heaps R_(s) ₁ , R_(s) ₂ , . . . ,R_(s) _(n)  5: Let L₁, L₂,. . . , L_(|u|) be the posting lists of terms in u, in the descendingorder of their maximal score  6: Let I₁, I₂, . . . , I_(|u|) be thetrees for the posting lists  7: A[s] ← 0 for all s — Accumulators vector 8: for iε[1, 2, . . . , |u|] do  9:$\left. {UB}\leftarrow{A_{1} + {\sum\limits_{j \geq i}{u_{j} \cdot {{ms}\left( L_{j} \right)}}}} \right.$10: pos ← I_(i) · next(1, UB) 11: while pos ≦ |L_(i)| do 12:⟨s, ps(s, i)⟩ ← posting  at  position  pos  in  L_(i) 13: A[s] ← A[s] +u_(i) · ps(s,i) 14: pos ← I_(i). next(pos,UB) 15: for every s such thatA[s] > 0 do 16: processScoredResult( s, u, A[s], R_(s), I) 17: returnR_(s) ₁ ,R_(s) ₂ , . . . , R_(s) _(n)

Algorithm 6 An example procedure that attempts to insert a Tweet u intoR_(s) and updates trees  1: ProcedureprocessScoredResult(s,u,score,R_(s),I)  2: μ_(s) ← min. score of a Tweetin R_(s) if |R_(s)| = k, 0 otherwise  3: if μ_(s) < score then  4: if|R_(s)| = k, then  5: Remove the least scored Tweet from R_(s)  6: Add(u, score) to R_(s)  7: μ′_(s) ← min. score of a Tweet in R_(s) if|R_(s)| = k, 0 otherwise  8: if μ′_(s) ≠ μ_(s) then  9: for j ∈ terms ofs do 10: I_(j). update(s, μ′_(s))

Let us next consider an example DAAT for publish-subscribe algorithm. ADAAT may, for example, provide an alternative strategy where the currenttop-k documents may be maintained as min-heap, and each documentencountered in one of the lists may be fully scored and considered forinsertion to the current top-k. Example Algorithm 7 (below) traversesthe posting lists in parallel, while each list maintains a “current”position. In this example, we denote a current position in list L byL.curPosition, a current story by L.cur, and a partial score of thecurrent story by L.curPs. In this example, a current story with a lowestid may be picked, scored and the lists where it was a current story maybe advanced to the next posting. A potential advantage compared to anexample TAAT may be that there may be no need to maintain a potentiallylarge set of partially accumulated scores.

Algorithm 7 Example DAAT for publish-subscribe Algorithm  1: Input:Index of S  2: Input: Query u  3: Input: R_(s) ₁ , R_(s) ₂ ,..., R_(s)_(n) —min-heaps of size k for all stories in S  4: Output: Updatedmin-heaps R_(s) ₁ , R_(s) ₂ ,..., R_(s) _(n)  5: Let L₁, L₂,..., L_(|u|)be the posting lists of terms in u  6: for i ∈ [1, 2,...,|u|] do  7:Reset the current position in L_(i) to the first posting  8: while notall lists exhausted do  9:  s ← min_(1≦i≦|u|) L_(i).cur 10:  score ← 011: for i ∈ [1, 2,...,|u|] do 12: if L_(i).cur = s then 13:  score ←score + u_(i) · L_(i).curPs 14: Advance by 1 the current position inL_(i) 15:  μ_(s) ← min. score of a Tweet in R_(s) if |R_(s)| = k, 0otherwise 16: if μ_(s) < score then 17: if |R_(s)| = k then 18: Removethe least scored Tweet from R_(s) 19: Add (u, score) to R_(s) 20: returnR_(s) ₁ , R_(s) ₂ ,..., R_(s) _(n)

Next let us consider an example skipping DAAT for publish-subscribealgorithm. Similarly to TAAT algorithms, it may be possible to skippostings in a DAAT based algorithm too. One popular algorithm is WAND(e.g., see, A. Z. Broder, et al., “Efficient Query Evaluation Using ATwo-Level Retrieval Process”, CIKM '03 Proceedings Of The TwelfthInternational Conference On Information And Knowledge Management, 2003).In each iteration WAND orders posting lists in an ascending order of thecurrent document id and looks for the pivot list—the first list L_(i)such that a sum of maximal scores in lists L_(i), . . . , L_(i−1) may bebelow a lowest score θ in a current top-k:

${\sum\limits_{j < i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}} \leq {\theta.}$

Then, for example, if the current document in the pivot list—the pivotdocument—equals to the current document in list L₁, the pivot documentmay be scored and considered for insertion into the current top-k.Otherwise, the current positions in lists L₁, . . . , L_(i−1) may, forexample, be skipped to a document id greater than or equal to the pivotdocument. This skipping is possible since by the ordering of the lists,and by definition of the pivot list, the maximal score of the documentswith ids lower than that of the pivot document may be below θ.

In certain example implementations herein, one may modify WAND'sskipping condition and skip only stories s in list L_(i) for which:

$\begin{matrix}{{\sum\limits_{j \leq i}{{u_{j} \cdot m}\;{s\left( L_{j} \right)}}} \leq {\mu_{s}.}} & {{Condition}\mspace{14mu}(2)}\end{matrix}$

In example Algorithm 8 (below) one may make use of a tree-basedtechnique to efficiently find, for every list L_(i), a first story froma current position in L_(i) onward that violates Condition (2). From aset of these stories one may, for example, choose a pivot story to be aminimal according to story id. The list containing the pivot story maythen, for example, be said to be the pivot list. Then, similar to WAND,the pivot story may be either scored and the processed Tweet u may beconsidered for insertion to R_(s), or the lists may be skipped to astory greater than or equal to the pivot story. As in the exampleskipping TAAT for publish-subscribe algorithm, example Algorithm 8 mayattempt to insert a Tweet into R_(s) of fully scored stories and updatesaffected trees.

Algorithm 8 Example skipping DAAT for publish-subscribe algorithm  1:Input: Index of S  2: Input: Query u  3: Input: R_(s) ₁ , R_(s) ₂ ,...,R_(s) _(n) —min-heaps of size k for all stories in S  4: Output: Updatedmin-heaps R_(s) ₁ , R_(s) ₂ ,..., R_(s) _(n)  5: Let L₁, L₂,..., L_(|u|)be the posting lists of terms in u  6: Let I₁, I₂,..., I_(|u|) be thetrees for the posting lists  7: for i ∈ [1, 2,...,|u|] do  8: Reset thecurrent position in L_(i) to the first posting  9: while true do 10:Sort posting lists in the ascending order of their current story ids 11: p ← ⊥ - index of the pivot list 12:  UB ← 0 13:  S ← L_(|u|).cur 14:for i ∈ [1, 2,...,|u|] do 15: if L_(i).cur ≧ s then 16: break 17:  UB ←UB + u_(i) · ms(L_(i)) 18:  pos ← I_(i).next(L_(i).curPosition, UB) 19:if pos ≦ |L_(i)| then 20: s′ ← story at position pos in L_(i) 21: if s′< s then 22: p ← i 23: s ← s′ 24:  if p =⊥ then 25: break 26: if L₀.cur≠ L_(p).cur then 27: for i ∈ [1, 2,..., p − 1]do 28: Skip the currentposition in L_(i) to a story ≧ s 29: else 30: score ← 0 31: i ← 0 32:while L_(i).cur = L_(p).cur do 33: score ← score + u_(i) · L_(i).curPs34: Advance by 1 the current position in L_(i) 35: i ← i + 1 36:processScoredResult( s,u,score,R_(s),I ) 37: return R_(s) ₁ , R_(s) ₂,..., R_(s) _(n)

As illustrated through the example implementations presented herein, itcan be seen that a publish-subscribe paradigm may be employed inmaintaining sets of publisher encoded data files that may be associatedwith subscriber encoded data files. Furthermore, the various resultingmethods and apparatuses may provide for real-time or near real-time use(e.g., annotation) of associated content in systems that may experiencea significantly high-volume of publisher encoded data files, subscriberencoded data files, and/or content requests.

In accordance with certain further aspects, example techniques providedherein may further be employed to establish a personalized micro-blog orsocial network feed and/or other like specific content alert capabilityby identifying content of interest, e.g., via one or more subscriberencoded data files. Hence, for example, a top-k result set of publisherencoded data files (e.g., Tweets, social commentary, etc.) may beidentified in response to an applicable content request.

Thus, as illustrated in various example implementations and techniquespresented herein, in accordance with certain aspects a method may beprovided for use as part of a special purpose computing device or otherlike machine that accesses digital signals from memory and processessuch digital signals to establish transformed digital signals which maythen be stored in memory.

Some portions of the detailed description have been presented in termsof processes or symbolic representations of operations on data signalbits or binary digital signals stored within memory, such as memorywithin a computing system or other like computing device. These processdescriptions or representations are techniques used by those of ordinaryskill in the data signal processing arts to convey the substance oftheir work to others skilled in the art. A process is here, andgenerally, considered to be a self-consistent sequence of operations orsimilar processing leading to a desired result. The operations orprocessing involve physical manipulations of physical quantities.Typically, although not necessarily, these quantities may take the formof electrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, data, values, elements, symbols, characters, terms,numbers, numerals or the like. It should be understood, however, thatall of these and similar terms are to be associated with the appropriatephysical quantities and are merely convenient labels. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “associating”, “identifying”, “determining”,“allocating”, “establishing”, “accessing”, “obtaining”, “maintaining”,“querying”, or the like refer to the actions or processes of a computingplatform, such as a computer or a similar electronic computing device(including a special purpose computing device), that manipulates ortransforms data represented as physical electronic or magneticquantities within the computing platform's memories, registers, or otherinformation (data) storage device(s), transmission device(s), or displaydevice(s).

According to an implementation, one or more portions of an apparatus,such as computing device 200 (FIG. 2), for example, may store binarydigital electronic signals representative of information expressed as aparticular state of the device, here, computing device 200. For example,an electronic binary digital signal representative of information may be“stored” in a portion of memory 204 by affecting or changing the stateof particular memory locations, for example, to represent information asbinary digital electronic signals in the form of ones or zeros. As such,in a particular implementation of an apparatus, such a change of stateof a portion of a memory within a device, such the state of particularmemory locations, for example, to store a binary digital electronicsignal representative of information constitutes a transformation of aphysical thing, here, for example, memory device 204, to a differentstate or thing.

The terms, “and”, “or”, and “and/or” as used herein may include avariety of meanings that also are expected to depend at least in partupon the context in which such terms are used. Typically, “or” if usedto associate a list, such as A, B or C, is intended to mean A, B, and C,here used in the inclusive sense, as well as A, B or C, here used in theexclusive sense. In addition, the term “one or more” as used herein maybe used to describe any feature, structure, or characteristic in thesingular or may be used to describe a plurality or some othercombination of features, structures or characteristics. Though, itshould be noted that this is merely an illustrative example and claimedsubject matter is not limited to this example.

While certain exemplary techniques have been described and shown hereinusing various methods and apparatuses, it should be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter.

Additionally, many modifications may be made to adapt a particularsituation to the teachings of claimed subject matter without departingfrom the central concept described herein. Therefore, it is intendedthat claimed subject matter not be limited to the particular examplesdisclosed, but that such claimed subject matter may also include allimplementations falling within the scope of the appended claims, andequivalents thereof.

What is claimed is:
 1. A method comprising: with a computing platform,generating and maintaining a subscriber index for one or more of aplurality of subscriber encoded content files comprising at leastinformational story content, and a publisher index for one or more of aplurality of publisher encoded content files comprising at leastmicro-blog and/or social network content; determining a content-basedscore and a recency score for the one or more of the plurality ofpublisher encoded content files for association with the one or more ofthe plurality of subscriber encoded content files, and using, at leastin part, the determined content-based score and the determined recencyscore to determine a ranking score for the one or more of the pluralityof publisher encoded content files for the association with the one ormore of the plurality of subscriber encoded content files; determiningfor the one or more of the plurality of subscriber encoded content filesa top-k set of publisher encoded content files based, at least in part,on the determined ranking score; in response to obtaining a newpublisher encoded content file: querying the subscriber index using atleast a portion of the new publisher encoded content file to determine aranking score for the new publisher encoded content file with regard tothe one or more of the plurality of subscriber encoded content files,wherein the ranking score is based, at least in part, on a content-basedscore and a recency score, wherein the recency score is based at leastin part on a difference between a time of page view and a time ofcreation of the new publisher encoded content file; and determiningwhether the new publisher encoded content file replaces one publisherencoded content file in the top-k set of publisher encoded content filesbased, at least in part, on a comparison of the ranking score of the newpublisher encoded content file and the ranking score of the top-k set ofpublisher encoded content files.
 2. The method as recited in claim 1,wherein the recency score decreases over a time-interval from the timeof creation to the time of page view, and the new publisher encodedcontent file is included in the top-k set of publisher encoded contentfiles providing the ranking score exceeds a threshold ranking scoreassociated with the top-k set of publisher encoded content files.
 3. Themethod as recited in claim 1, and further comprising, with the computingplatform: in response to a request for the at least one of the pluralityof subscriber encoded content files, identifying the top-k set ofpublisher encoded content files.
 4. The method as recited in claim 1,wherein determining the top-k set of publisher encoded content filesfurther comprises: ranking at least the publisher encoded content files.5. The method as recited in claim 1, wherein determining the top-k setof publisher encoded content files further comprises: determining thetop-k set of publisher encoded content files using a top-k retrievalpublish-subscribe process comprising at least one of: a term-at-a-time(TAAT) publish-subscribe process; a skipping TAAT publish-subscribeprocess; a document-at-a-time (DAAT) publish-subscribe process; or askipping DAAT publish-subscribe process.
 6. The method of claim 1,wherein the indices are generated at least in part based on anindication of content relevancy.
 7. The method of claim 1, whereinreplacing one publisher encoded content file in the top-k set ofpublisher encoded content files comprises: comparing the ranking scoreof the new publisher encoded content file with the ranking score of oneor more publisher encoded content files of the top-k set of publisherencoded content files, and responsive to the comparison, removing apublisher encoded content file with a lower ranking score from the top-kset of publisher encoded content files and adding the new publisherencoded content file to the top-k set of publisher encoded contentfiles.
 8. The method of claim 7, further comprising: receiving a newsubscriber encoded content file; querying the publisher index based, atleast in part, on a portion of the new subscriber encoded content file;and retrieving a set of publisher encoded content files responsive tothe query.
 9. A computing platform comprising: memory; and a processingunit to: generate and maintain, in the memory, a subscriber index forone or more of a plurality of subscriber encoded content files tocomprise at least informational story content, and a publisher index forone or more of a plurality of publisher encoded content files tocomprise at least micro-blog and/or social network content; determine acontent-based score and a recency score for the one or more of theplurality of publisher encoded content files for association with theone or more of the plurality of subscriber encoded content files, anduse, at least in part, the to be determined content-based score and theto be determined recency score to determine a ranking score for the oneor more of the plurality of publisher encoded content files for theassociation with the one or more of the plurality of subscriber encodedcontent files; determine for the one or more of the plurality ofsubscriber encoded content files a top-k set of publisher encodedcontent files to be based at least in part on the to be determinedranking score; in response to a new publisher encoded content file:query the subscriber index, at least a portion of the new publisherencoded content file to be employed to determine a ranking score for thenew publisher encoded content file with regard to the one or more of theplurality of subscriber encoded content files, wherein the ranking scoreis to be based, at least in part, on a content-based score and a recencyscore, wherein the recency score is to be based at least in part on adifference between a time of page view and a time of creation of the newpublisher encoded content file; and determine whether the new publisherencoded content file is to replace one publisher encoded content file inthe top-k set of publisher encoded content files to be based, at leastin part, on a comparison of the ranking score of the new publisherencoded content file and the ranking score of the top-k set of publisherencoded content files.
 10. The computing platform as recited in claim 9,the processing unit to further: in response to a request for the atleast one of the plurality of subscriber encoded content files, identifythe top-k set of publisher encoded content files.
 11. The computingplatform as recited in claim 9, the processing unit to further:determine the top-k set of publisher encoded content files from at leastthe publisher encoded content files to be ranked.
 12. The computingplatform as recited in claim 9, the processing unit to further:determine the top-k set of publisher encoded content files to compriseuse of a top-k retrieval publish-subscribe process to comprise at leastone of: a term-at-a-time (TAAT) publish-subscribe process; a skippingTAAT publish-subscribe process; a document-at-a-time (DAAT)publish-subscribe process; or a skipping DAAT publish-subscribe process.13. The computing platform of claim of claim 9, the processing unit togenerate the indices to be based at least in part on an indication ofcontent relevancy.
 14. The computing platform of claim 9, wherein toreplace one publisher encoded content file in the top-k set of publisherencoded content files is to: compare the ranking score of the newpublisher encoded content file with the ranking score of one or morepublisher encoded content files of the top-k set of publisher encodedcontent files, and responsive to the comparison, remove a publisherencoded content file with a lower ranking score from the top-k set ofpublisher encoded content files and add the new publisher encodedcontent file to the top-k set of publisher encoded content files.
 15. Anarticle computing: a non-transitory computer readable medium havingstored therein computer implementable instructions executable by aprocessing unit to: generate and maintain a subscriber index for one ormore of a plurality of subscriber encoded content files to comprise atleast informational story content, and a publisher index for one or moreof the plurality of publisher encoded content files to comprise at leastmicro-blog and/or social network content; determine a content-basedscore and a recency score for the one or more of the plurality ofpublisher encoded content files for association with the one or more ofthe plurality of subscriber encoded content files, and use, at least inpart, the to be determined content-based score and the to be determinedrecency score to determine a ranking score for the one or more of theplurality of publisher encoded content files for the association withthe one or more of the plurality of subscriber encoded content files;determine for the one or more of the plurality of subscriber encodedcontent files a top-k set of publisher encoded content files to bebased, at least in part, on the to be determined ranking score; inresponse to a new publisher encoded content file: query the subscriberindex, at least a portion of the new publisher encoded content file tobe employed to determine a ranking score for the new publisher encodedcontent file with regard to the one or more of the plurality ofsubscriber encoded content files, wherein the ranking score is to bebased, at least in part, on a content-based score and a recency score,wherein the recency score is to be based at least in part on adifference between a time of page view and a time of creation of the newpublisher encoded content file; and determine whether the new publisherencoded content file is to replace one publisher encoded content file inthe top-k set of publisher encoded content files to be based, at leastin part, on a comparison of the ranking score of the new publisherencoded content file and the ranking score of the top-k set of publisherencoded content files.
 16. The article as recited in claim 15, thecomputer implementable instructions being further executable by theprocessing unit to: in response to a request for the at least one of theplurality of subscriber encoded content files, identify the top-k set ofpublisher encoded content files.
 17. The article as recited in claim 15,the computer implementable instructions being further executable by theprocessing unit to: determine the top-k set of publisher encoded contentfiles from at least the publisher encoded content files to be ranked.18. The article as recited in claim 15, the computer implementableinstructions being further executable by the processing unit to:determine the top-k set of publisher encoded content files to compriseuse of a top-k retrieval publish-subscribe process to comprise at leastone of: a term-at-a-time (TAAT) publish-subscribe process; a skippingTAAT publish-subscribe process; a document-at-a-time (DAAT)publish-subscribe process; or a skipping DAAT publish-subscribe process.19. The article of claim 15, wherein the instructions are furtherexecutable to generate the indices to be based at least in part on anindication of content relevancy.
 20. The article of claim 15, wherein toreplace one publisher encoded content file in the top-k set of publisherencoded content files the computer implementable instructions are to befurther executable by the processing unit to: compare the ranking scoreof the new publisher encoded content file with the ranking score of oneor more publisher encoded content files of the top-k set of publisherencoded content files, and responsive to the comparison, remove apublisher encoded content file with a lower ranking score from the top-kset of publisher encoded content files and add the new publisher encodedcontent file to the top-k set of publisher encoded content files.